Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments

Abstract In spite of the remarkable development of technology, most studies for building energy controls to evaluate or estimate the energy performance have not accurately reflected actual building’s energy consumption patterns. For this issue, several techniques, such as simulation and calibration, comprehensive survey system, smart metering, and commissioning, have been attempted. However, in most studies, some factors in thermal systems derived from occupant behavior were perceived as fixed objects, and the factors were converted into simple numbers as parts of inputs into simulation templates. There was lack of studies on considerations that unpredictable responses derived from human anti-logic or common sense could deteriorate energy efficiency in theoretical analyses even though the systems were properly operated. This research proposes integrated energy supply models based on artificial intelligence responding to anti-logic or common sense that can reduce machine’s energy saving effects. By use of design scenarios assuming some unusual situations, a decision making model determines the extent to which the cause of the abnormal situations are associated with the occupant behavior. After the five-step phases in the decision making model, the actual outputs of the energy supply model for the buildings are determined, and the reciprocal communication between the thermal and decision making models mitigates thermal dissatisfaction and energy inefficiency. Comparative analysis describes the decision making model’s effectiveness that it improves thermal comfort levels by about 2.5% for an office building and about 10.2% for residential buildings, and that it reduces annual energy consumption by about 17.4% for an office building and about 25.7% for residential buildings. As a consequence, the integrated energy control model has advantages that it noticeably improves thermal comfort and energy efficiency, and that it properly respond to abnormal and abrupt indoor situations derived from human anti-logic or common sense.

[1]  G. Lachiver,et al.  A fuzzy control system based on the human sensation of thermal comfort , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[2]  Peng Xu,et al.  Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data , 2016 .

[3]  Rafael Alcalá,et al.  Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms , 2003, Applied Intelligence.

[4]  Basil Kouvaritakis,et al.  Application of generalised predictive control to a boiler-turbine unit for electricity generation , 1990 .

[5]  Claudio Maffezzoni,et al.  Boiler-turbine dynamics in power-plant control , 1996 .

[6]  Xiufeng Pang,et al.  Monitoring-based HVAC commissioning of an existing office building for energy efficiency , 2013 .

[7]  J. Clinch,et al.  Fuel poverty, thermal comfort and occupancy: results of a national household-survey in Ireland , 2002 .

[8]  Jiří Jaromír Klemeš,et al.  Integrating district cooling systems in Locally Integrated Energy Sectors through Total Site Heat Integration , 2016 .

[9]  Steve Marks,et al.  DUSEL-related Science at LBNL -- Program and Opportunities , 2010 .

[10]  Mary Ann Piette,et al.  A pattern-based automated approach to building energy model calibration , 2016 .

[11]  Svend Svendsen,et al.  Method for simulating predictive control of building systems operation in the early stages of building design , 2011 .

[12]  Hossam A. Gabbar,et al.  Comfort-based fuzzy control optimization for energy conservation in HVAC systems , 2014 .

[13]  Sarah C. Darby,et al.  Making it Obvious: Designing Feedback into Energy Consumption , 2001 .

[14]  Danielle Monfet,et al.  Ongoing commissioning of water-cooled electric chillers using benchmarking models , 2012 .

[15]  José A. Orosa,et al.  Thermal comfort and energy consumption in modern versus traditional buildings in Cameroon: A questionnaire-based statistical study , 2014 .

[16]  Francisco Herrera,et al.  A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions , 2013, IEEE Transactions on Fuzzy Systems.

[17]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[18]  Jonghoon Ahn,et al.  Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands , 2017 .

[19]  James E. Braun,et al.  Evaluating the Performance of Building Thermal Mass Control Strategies , 2001 .

[20]  D. P. Atherton,et al.  Automatic tuning of optimum PID controllers , 1993 .

[21]  Jörn von Grabe,et al.  Potential of artificial neural networks to predict thermal sensation votes , 2016 .

[22]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[23]  Fu Xiao,et al.  Performance assessment of district cooling systems for a new development district at planning stage , 2015 .

[24]  Andrea Costa,et al.  Model calibration for building energy efficiency simulation , 2014 .

[25]  Per Heiselberg,et al.  Control strategies for intelligent glazed façade and their influence on energy and comfort performance of office buildings in Denmark , 2015 .

[26]  M. Zaheeruddin,et al.  Hybrid fuzzy logic control strategies for hot water district heating systems , 2007 .

[27]  Roberto Carapellucci,et al.  Upgrading existing coal-fired power plants through heavy-duty and aeroderivative gas turbines , 2015 .

[28]  Xiao Chen,et al.  Occupant feedback based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation , 2016 .

[29]  Tony N.T. Lam,et al.  Artificial neural networks for energy analysis of office buildings with daylighting , 2010 .

[30]  L. Gustavsson,et al.  Minimum-cost district heat production systems of different sizes under different environmental and social cost scenarios , 2014 .

[31]  Burcin Becerik-Gerber,et al.  A model calibration framework for simultaneous multi-level building energy simulation , 2015 .

[32]  Servet Soyguder,et al.  Predicting of fan speed for energy saving in HVAC system based on adaptive network based fuzzy inference system , 2009, Expert Syst. Appl..